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Abstract:

An advertisement impression distribution system is programmed to generate
an allocation plan for serving a number of advertisement impressions
changeable as a result of one or more events, the allocation plan to
allocate a first portion of advertisement impressions to satisfy
guaranteed demand and a second portion of advertisement impressions to
satisfy non-guaranteed demand. The system includes an optimizer
programmed to establish a relationship between the first portion of
advertisement impressions and the second portion of advertisement
impressions, the relationship defining a range of possible proportions of
allocation of the first portion of advertisement impressions and the
second portion of advertisement impressions; and to impose at least one
objective on the relationship including moderating an increase in the
number of advertisement impressions available for allocation to the first
and second portions, to minimize a cost associated with reducing a
quality of the advertisement impressions as their volume increases. The
system outputs the allocation plan to an ad serving module to control
serving of the advertisement impressions according to the range of
possible proportions of allocation between the first and the second
portions.

Claims:

1. An advertisement impression distribution system, comprising: a data
processing system including a processor and memory, the data processing
system programmed to generate an allocation plan for serving a number of
advertisement impressions eligible to meet a demand of a plurality of
advertisers, the allocation plan to allocate a first portion of
advertisement impressions to satisfy guaranteed demand and a second
portion of advertisement impressions to satisfy non-guaranteed demand,
the number of advertisement impressions being changeable as a result of
one or more events; where the data processing system includes an
optimizer, the optimizer programmed to: establish a relationship between
the first portion of advertisement impressions and the second portion of
advertisement impressions, the relationship defining a range of possible
proportions of allocation of the first portion of advertisement
impressions and the second portion of advertisement impressions; and
impose at least one objective on the relationship, wherein the at least
one objective comprises moderating an increase in the number of
advertisement impressions available for allocation to the first and
second portions, to minimize a cost associated with reducing a quality of
the advertisement impressions as their volume increases; where the data
processing system is further programmed to output the allocation plan to
an ad serving module of the data processing system to control serving of
the advertisement impressions according to the range of possible
proportions of allocation between the first and the second portions.

2. The system of claim 1, where the one or more events that cause the
advertisement impressions to change comprise one or more of: changing a
score threshold for qualifying a user into a specified interest category;
changing navigational links on a web page; and dynamically changing
displayed content on a web page; where the interest category depends on
user behavior, and where the cost of the advertisement impressions
comprises a cost associated with a function that increases with an
increase in the number of advertisement impressions.

3. The system of claim 1, where the at least one objective comprises
minimizing a supply cost of the advertisement impressions, the optimizer
further programmed to impose one or more of the following objectives in
the relationship: maximizing guaranteed demand representativeness,
maximizing non-guaranteed demand revenue, and minimizing under-delivery
penalties.

4. The system of claim 3, where the allocation plan is generated as goal
programming, the optimizer further programmed to: solve for a first of
the objectives, resulting in a first requirement, then solve for a second
of the objectives to generate a second requirement while relaxing the
first requirement, where relaxing is to allow departure from a determined
optimum value.

5. The system of claim 4, where the first requirement comprises a minimum
penalty cost and the second requirement comprises a maximum
non-guaranteed demand revenue, and where relaxing the first requirement
comprises allowing the first requirement to be greater than the solved
minimum penalty cost.

6. The system of claim 4, where the optimizer is further programmed to:
solve a third objective to generate a third requirement while relaxing
one or more of the first and second requirements.

7. The system of claim 6, where the first requirement comprises a maximum
non-guaranteed demand revenue, the second requirement comprises a minimum
penalty cost, and the third requirement comprises a maximum guaranteed
demand representativeness, and where relaxing the one or more of the
first and second requirements comprises one or more of: allowing the
first requirement to be less than the solved maximum non-guaranteed
demand revenue; and allowing the second requirement to be greater than
the solved minimum penalty cost.

8. The system of claim 6, further comprising: solving a fourth objective
to generate a fourth requirement while relaxing one or more of the first,
second, and third requirements.

9. The system of claim 8, where the first, second, and third requirements
comprise a minimum penalty cost, a maximum non-guaranteed demand revenue,
and a minimum supply cost of the advertising impressions in any order,
and where the fourth requirement comprises a maximum guaranteed demand
representativeness.

10. The system of claim 9, where the first through fourth objectives are
ordered according to priority and the highest priority objective is
solved first.

11. The system of claim 3, where the optimizer is further programmed to:
combine the under-delivery penalties, the non-guaranteed demand revenue,
and the supply cost of the advertisement impressions using a weighted sum
of monetary objectives, the method further including: first optimizing
the monetary objectives; and next optimizing the guaranteed demand
representativeness.

12. A method for distributing advertisement impressions, the method
executable by a data processing system having a processor and memory, and
in the memory stored instructions, comprising: generating, with the
system through execution of the instructions, an allocation plan for
serving a number of advertisement impressions eligible to meet a demand
of a plurality of advertisers, the allocation plan to allocate a first
portion of advertisement impressions to satisfy guaranteed demand and a
second portion of advertisement impressions to satisfy non-guaranteed
demand, the number of advertisement impressions being changeable as a
result of one or more events, where generating comprises: establishing a
relationship between the first portion of advertisement impressions and
the second portion of advertisement impressions, the relationship
defining a range of possible proportions of allocation of the first
portion of advertisement impressions and the second portion of
advertisement impressions; and imposing at least one objective on the
relationship, wherein the at least one objective comprises moderating an
increase in the number of advertisement impressions available for
allocation to the first and second portions, to minimize a cost
associated with reducing a quality of the advertisement impressions as
their volume increases; and outputting the allocation plan to an ad
serving module of the system through execution of the instructions, to
control serving of the advertisement impressions according to the range
of possible proportions of allocation between the first and the second
portions.

13. The method of claim 12, where the one or more events that cause the
advertisement impressions to change comprise one or more of: changing a
score threshold for qualifying a user into a specified interest category;
changing navigational links on a web page; and dynamically changing
displayed content on a web page; where the interest category depends on
user behavior, and where the cost of the advertisement impressions
comprises a cost associated with a function that increases with an
increase in the number of advertisement impressions.

14. The method of claim 12, where the at least one objective comprises
minimizing a supply cost of the advertisement impressions, the method
further comprising also imposing one or more of the following objectives
in the relationship: maximizing guaranteed demand representativeness,
maximizing non-guaranteed demand revenue, and minimizing under-delivery
penalties.

15. The method of claim 14, where the allocation plan is generated as
goal programming, the method further comprising: solving for a first of
the objectives, resulting in a first requirement, followed by solving for
a second of the objectives to generate a second requirement while
relaxing the first requirement, where relaxing is to allow departure from
a determined optimum value.

16. The method of claim 15, where the first requirement comprises a
minimum penalty cost and the second requirement comprises a maximum
non-guaranteed demand revenue, and where relaxing the first requirement
comprises allowing the first requirement to be greater than the solved
minimum penalty cost.

17. The method of claim 15, further comprising: solving a third objective
to generate a third requirement while relaxing one or more of the first
and second requirements.

18. The method of claim 17, where the first requirement comprises a
maximum non-guaranteed demand revenue, the second requirement comprises a
minimum penalty cost, and the third requirement comprises a maximum
guaranteed demand representativeness, and where relaxing the one or more
of the first and second requirements comprises one or more of: allowing
the first requirement to be less than the solved maximum non-guaranteed
demand revenue; and allowing the second requirement to be greater than
the solved minimum penalty cost.

19. The method of claim 17, further comprising: solving a fourth
objective to generate a fourth requirement while relaxing one or more of
the first, second, and third requirements.

20. The method of claim 19, where the first, second, and third
requirements comprise a minimum penalty cost, a maximum non-guaranteed
demand revenue, and a minimum supply cost of the advertising impressions
in any order, and where the fourth requirement comprises a maximum
guaranteed demand representativeness.

21. The method of claim 19, where the first through fourth objectives are
ordered according to priority and the highest priority objective is
solved first.

22. The method of claim 14, further comprising: combining the
under-delivery penalties, the non-guaranteed demand revenue, and the
supply cost of the advertisement impressions using a weighted sum of
monetary objectives, the method further including: first optimizing the
monetary objectives; and next optimizing the guaranteed demand
representativeness.

Description:

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is related to U.S. patent application Ser. No.
12/609,396, filed Oct. 30, 2008, which is a continuation-in-part of U.S.
patent application Ser. No. 12/241,657, filed Sep. 30, 2008, the entirety
of both of which is incorporated by reference herein.

[0003] A market exists for the distribution of advertising and other
information over data communications and entertainment networks. A
non-limiting example is insertion of advertising copy supplied by
advertisers for appearance on web pages having content offered by media
distributors such as news and information services, internet service
providers, and suppliers of products related to the products or services
of the advertiser.

[0004] The value of an opportunity to present an ad (i.e., to exploit an
"ad impression") is different for different advertisers and different web
page or entertainment genres, because the content of the media delivered
by a particular media outlet draws users of a certain type that may
correlate more or less strongly with a population of potential customers
that an advertiser seeks to reach. Variation in the value of ads, the
ability to discriminate among ad recipients as a function of the variable
content of the web pages they access, and the ability to shift
selectively to route appropriate ad content to a selected user when a web
page is rendered all make on-line network communications a useful and
efficient environment for advertising, and especially for targeted
advertising.

[0005] The network could be the Worldwide Web, and the advertising copy
could comprise banner ads, graphics in fields of specific size and
placement, overlaid moving pictures or animation, redirection to a
different URL, etc. The same targeting abilities also are applicable to
networks that are interactive to a lesser degree, such as cable
television ad insertion, which might be done at a head end or at a hub,
or even from a subscriber-specific set top box.

[0006] Accordingly, ad impressions may be delivered in a manner to target
characteristics (or attributes) of users or web page content--referred to
as representativeness--with the goal to fairly represent the targeted
characteristics in delivered impressions. When advertisers pay in advance
by way of contract for a specific number of ad impressions, they desire a
certain quality of representativeness, which creates what is termed
guaranteed demand (GD). Normally, ad impressions are first served to
satisfy GD contracts. Ad impressions may also be auctioned via an ad
exchange on an ad hoc spot market when the ad impressions exceed
projected guaranteed demands or are more profitably auctioned, which
creates non-guaranteed demand (NGD). Availability of NGD ad impressions
is generally resolved immediately before advertisement delivery in real
time.

[0007] Optimizing a balance between meeting the guaranteed (GD) demand in
a representative way that best targets user characteristics with a desire
to increase NGD revenue and minimize penalties associated with
under-delivery of GD ad impressions can be achieved through programmable
modeling.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The system and/or method may be better understood with reference to
the following drawings and description. Non-limiting and non-exhaustive
descriptions are provided with reference to the following drawings. The
components in the figures are not necessarily to scale, emphasis instead
being placed upon illustrating principles. In the figures, like
referenced numerals may refer to like parts throughout the different
figures unless otherwise specified.

[0009] FIG. 1 is a block diagram of a general overview of a network
environment and system for distributing advertisement impressions.

[0010] FIG. 2 is a flow/block diagram illustrating a method and system to
support a marketing relationship among advertisers, media outlets and an
ad distribution system.

[0011] FIG. 3 is a block diagram of an exemplary architecture for
advertising delivery systems.

[0013]FIG. 5 is a graphic depiction of a cost of supply function when
supply volume of ad impressions fluctuates.

[0014]FIG. 6 is a graphic depiction of an under-delivery penalty
function.

[0015]FIG. 7 is a graphic depiction of a non-guaranteed cost and revenue
function.

[0016] FIG. 8 is a flow chart of a method for distributing advertisement
impressions through an exchange in which the number of ad impressions is
changeable.

[0017]FIG. 9 is an exemplary processing system for implementing the
advertisement impression distribution systems and methods.

DETAILED DESCRIPTION

[0018] A supply of ad impression opportunities preferably comprises
opportunities to insert on-line advertising ("ad impressions"), such as
inserting variable banner ads into web pages that are transmitted to
users. The ads can be allocated selectively, based on characteristics of
the user or typical users of the particular web page, or otherwise
selected to match user and content information, location, timing and
other criteria to advertiser specifications, for targeting the ads to
potential customers or web users.

[0019] The allocation of increments of a supply of advertisements to meet
demand may be optimized in a market for use of advertising opportunities
(ad impressions) by establishing a proportion of revenue and/or quantity
to be shared between distinct categories of demand with potentially
different marginal values. A programmable technique divides all
allocations that are projected and later the allocations that actually
arise, between a category of pre-committed increments, typically
contractually committed ad insertion opportunities with predetermined
characteristics (e.g., guaranteed delivery (GD) of ads contracts), and a
category of spot sales, such as via ad exchange auctions (e.g.,
non-guaranteed delivery (NGD) delivery of ads).

[0020] To increase opportunities for revenue, the ad impression supply
volume for meeting all demands may be increased, for instance by
broadening the characteristics targeted by advertisers. Other variables,
such as changing web page content or navigational links on web pages, may
similarly cause the ad impressions to change in volume. Additionally, ad
impressions may change when writers are solicited to write articles about
a particular topic (e.g., via an associated content platform).

[0021] The supply volume of ad impressions, however, involves a cost when
increased beyond a predetermined level, as will be discussed later in
more detail. For instance, if a threshold score for qualifying users into
specified interest categories is changed such as to capture more ad
impressions, the return on investment (ROI) advertisers see from ads
delivered to those impressions drops as the ad impressions become
increasingly less targeted to the profile of the advertiser. Delivered
ads that are less targeted are lower in quality and will likely garner
fewer clicks, thus reducing the advertiser's click-through-rate (CTR) on
affected advertisements. The disclosed system may automate the process by
which ad impressions are advantageously increased so as to generate more
revenue for publishers and brokers, such as Yahoo! of Sunnyvale, Calif.,
but to moderate that increase so as to minimize costs associated with
changeable supply of ad impressions. Those costs may be seen in lost
advertisers or fewer contracts with advertisers due to a reduction in
their ROI, for instance, and thus may be viewed as lost opportunity
costs.

[0022] A multi-objective approach to optimizing representativeness is
modeled herein that seeks to minimize the cost of a changeable volume of
ad impressions and to minimize penalties due to under-delivery of ad
impressions to satisfy GD contracts while maximizing, in a balanced
approach, both GD representativeness and NGD demand revenue. This
optimization will take place with reference to buying and selling ad
impressions in an advertising marketplace including an ad exchange where
over-delivery and under-delivery are also modeled.

[0023] FIG. 1 provides a simplified view of a network environment 100 for
serving advertisement impressions, factoring both guaranteed demand and
non-guaranteed demand, in an optimized way. Not all of the depicted
components may be required, however, and some implementations may include
additional components not shown in the Figure. Variations in the
arrangement and type of the components may be made without departing from
the spirit or scope of the claims as set forth herein. Additional,
different or fewer components may be provided.

[0024] The network environment 100 may include an administrator 110 and
one or more users 120A-N with access to one or more networks 130, 135,
and one or more web applications, standalone applications, mobile
applications 115, 125A-N, which may collectively be referred to as client
applications. The network environment 100 may also include one or more
advertisement servers 140 and related data stores 145, and one or more
optimizer servers 150 and related data stores 155. The users 120 A-N may
request pages, such as web pages, via the web application, standalone
application, mobile application 125 A-N, such as web browsers. The
requested page may request an advertisement impression from the
advertisement server 140 to fill a space on the page. The advertiser
server 140 may serve one or more advertisement impressions to the pages
in accordance with delivery instructions from the optimizer server 150.
The advertisement impressions may include online graphical
advertisements, such as in a unified marketplace for graphical
advertisement impressions. Some or all of the advertisement server 140,
the optimizer server 150, and the one or more web applications,
standalone application, mobile applications 115, 125A-N, may be in
communication with each other by way of the networks 130 and 135.

[0025] The networks 130, 135 may include wide area networks (WAN), such as
the Internet, local area networks (LAN), campus area networks,
metropolitan area networks, or any other networks that may allow for data
communication. The network 130 may include the Internet and may include
all or part of network 135; network 135 may include all or part of
network 130. The networks 130, 135 may be divided into sub-networks. The
sub-networks may allow access to all of the other components connected to
the networks 130, 135 in the network environment 100, or the sub-networks
may restrict access between the components connected to the networks 130,
135. The network 135 may be regarded as a public or private network
connection and may include, for example, a virtual private network or an
encryption or other security mechanism employed over the public Internet,
or the like.

[0026] The web applications, standalone applications and mobile
applications 115, 125A-N may be connected to the network 130 in any
configuration that supports data transfer. This may include a data
connection to the network 130 that may be wired or wireless. Any of the
web applications, standalone applications and mobile applications 115,
125A-N may individually be referred to as a client application. The web
application 125A may run on any platform that supports web content, such
as a web browser or a computer, a mobile phone, personal digital
assistant (PDA), pager, network-enabled television, digital video
recorder, such as TIVO®, automobile and/or any appliance or platform
capable of data communications.

[0027] The standalone application 125B may run on a machine that includes
a processor, memory, a display, a user interface and a communication
interface. The processor may be operatively connected to the memory,
display and the interfaces and may perform tasks at the request of the
standalone application 125B or the underlying operating system. The
memory may be capable of storing data including programmable instructions
or computer code. The display may be operatively connected to the memory
and the processor and may be capable of displaying information to the
user B 125B. The user interface may be operatively connected to the
memory, the processor, and the display and may be capable of interacting
with a user B 120B. The communication interface may be operatively
connected to the memory, and the processor, and may be capable of
communicating through the networks 130, 135 with the advertisement server
140. The standalone application 125B may be programmed in any programming
language that supports communication protocols. These languages may
include: SUN JAVA®, C++, C#, ASP, SUN JAVASCRIPT®, asynchronous
SUN JAVASCRIPT®, or ADOBE FLASH ACTIONSCRIPT®, ADOBE FLEX®,
and others.

[0028] The mobile application 125N may run on any mobile device that may
have a data connection. The data connection may be a cellular connection,
a wireless data connection, an internet connection, an infra-red
connection, a Bluetooth connection, or any other connection capable of
transmitting data. For example, the mobile application 125N may be an
application running on an APPLE IPHONE®.

[0029] The advertisement server 140 may include one or more of the
following: an application server, a mobile application server, a data
store, a database server, and a middleware server. The advertisement
server 140 may exist on one machine or may be running in a distributed
configuration on one or more machines. The advertisement server 140 may
be in communication with the client applications 115, 125A-N, such as
over the networks 130, 135. For example, the advertisement server 140 may
provide a user interface to the users 120A-N through the client
applications 125A-N, such as a user interface for inputting search
requests and/or viewing web pages. Alternatively or in addition, the
advertisement server 140 may provide a user interface to the
administrator 110 via the client application 115, such as a user
interface for managing the data source 145 and/or configuring
advertisements.

[0030] The service provider server 140 and client applications 115, 125A-N
may be one or more computing devices of various kinds, such as the
computing device in FIG. 9. Such computing devices may generally include
any device that may be configured to perform computation and that may be
capable of sending and receiving data communications by way of one or
more wired and/or wireless communication interfaces. Such devices may be
configured to communicate in accordance with any of a variety of network
protocols, including but not limited to protocols within the Transmission
Control Protocol/Internet Protocol (TCP/IP) protocol suite. For example,
the web application 125A may employ the Hypertext Transfer Protocol
("HTTP") to request information, such as a web page, from a web server,
which may be a process executing on the advertisement server 140.

[0031] There may be several configurations of database servers,
application servers, mobile application servers, and middleware
applications included in the advertisement server 140. The data store 145
may be part of the advertisement server 140 and may be a database server,
such as MICROSOFT SQL SERVER®, ORACLE®, IBM DB2®,
SQLITE®, or any other database software, relational or otherwise. The
application server may be APACHE TOMCAT®, MICROSOFT IIS®, ADOBE
COLDFUSION®, or any other application server that supports
communication protocols.

[0032] The networks 130, 135 may be configured to couple one computing
device to another computing device to enable communication of data
between the devices. The networks 130, 135 may generally be enabled to
employ any form of machine-readable media for communicating information
from one device to another. Each of the networks 130, 135 may include one
or more of a wireless network, a wired network, a local area network
(LAN), a wide area network (WAN), a direct connection such as through a
Universal Serial Bus (USB) port, and the like, and may include the set of
interconnected networks that make up the Internet. The networks 130, 135
may include communication methods by which information may travel between
computing devices.

[0033] FIG. 2 is a flow/block diagram illustrating a method and system to
support a marketing relationship among advertisers 200, media outlets 230
and an ad distribution system 250 herein described. Inasmuch as the ad
impressions that are to be used to meet the representative demand profile
arise over time, an agreement to exploit the ad impressions may rely
partly on an estimation of the number and character of ad impressions
that arise. If a media outlet is reasonably sure that a given number of
ad impressions of a given type arise, then the media outlet can commit
contractually to using the ad impressions to meet the demand of
particular advertisers whose representative profile encompasses ad
impressions of that type. In an advertising contract, it is possible for
parties to agree to a "best efforts" obligation to produce exploitable ad
impressions, but a contract containing obligations to produce a certain
number and type of ad impressions may be preferable. In that case, the
guaranteed ad impressions (guaranteed deliver GD ads) can command a
better price than potential ad impressions that might be subject to
contract but are not guaranteed (non-guaranteed delivery NGD ads) and are
uncertain to arise at all.

[0034] This situation may be handled in advertising systems by selling
guaranteed ad impressions in advance, and selling the additional ad
impressions that may arise under different contractual provisions and
effectively in a substantially independent market. The present ad
distribution system is configured to aid in unifying these two different
markets.

[0035] An efficient and organized technique may satisfy the demand for
distribution of advertising to users. The users can be more or less
specifically defined by user characteristics. From the advertisers'
perspective, an objective is to enable ads to be targeted to users as a
function of the users' characteristics. The users' likely characteristics
are known to the media outlets that serve the users, at least because
user characteristics correlate with the content of media outlets that the
users visit. Often the media outlets may have access to additional
subscriber information from browsing history, stored cookies and other
factors. The advertisers have preferences and rules for distribution of
ads, that may include guidelines based on likely user characteristics and
also rules for spreading advertising coverage over a range of users. All
such rules, guidelines and preferences on the part of an advertiser,
which might result from studies and marketing plans, together define a
representative profile of the advertising demand of that advertiser. An
advertiser's representative demand profile corresponds to a subset of all
opportunities that might become available to insert and display an ad
(all the "ad impressions"), and may include some insertions that are of
more value to the advertiser than others.

[0036] Likewise, from knowledge of user characteristics and from
projections of the likely range of users who may be interested enough to
visit a certain type of media content in the future, the media outlets
can make estimates of the numbers and characteristics of users that are
likely to be subject to advertising impressions that might be devoted to
displaying an advertiser's content. There is a supply and demand market
involving discriminating for ad impressions that meet an advertiser's
representative demand profile, allocating and using the ad impressions
that arise to meet incremental parts of the representative demand,
reporting to the advertiser and collecting revenue in exchange for this
service.

[0037] In FIG. 2, the advertisers 200 define a representative demand
profile that they deem to be appropriate. The advertisers 200 might study
their products, commission surveys, collect information from actual
customers and so forth, to identify likely targets for ads for a
particular product or ads written perhaps to be appealing to some
recipients more than others. The advertisers 200 typically have various
rules for associating ads with ad impressions of distinct types, and for
distributing ads generally over various subsets of a population, not
necessarily limited to applying their advertising expenditures only to
certain targeted subsets. All such rules and associations make up a
representative profile that can be unique to an advertiser or an
advertised product.

[0038] The media outlets 230 also collect information about their user
base and the patterns of user access to and usage of media of one content
or another. The media outlets have knowledge of the content of the media
and also have knowledge of their users' patterns of access. The media
outlets may have subscriber information such as location and demographic
data. Some subscriber information can be inferred from a user's access to
certain content. All this information is collected and used to study and
associate patterns of subscribers and content so as to provide knowledge
of the opportunities available to insert advertising that may be of
interest to users.

[0039] The information collected by media outlets enables projections to
estimate the nature and number of ad impressions that are likely to
become available at a given time. The information can include, for
example, an estimated number of users having defined characteristics who
are projected to access a particular web page or other media content
source over a given time window. Depending on the information collected,
the defined user characteristics might include measures of age, gender,
income, family associations, etc., with statistical ranges of confidence
in the values.

[0040] As a result of collection and study of information, the media
outlets or their nominee can determine and define a projected probable
inventory of ad impressions that may be offered for sale to advertisers.
Within statistical limits, the media outlets may believe that an excess
inventory of ad impressions may be available. However, the media outlets
are not likely to commit as readily to sale of ad impressions under
contracts guaranteeing delivery or containing non-performance penalties,
when the availability of the excess ad impressions is unsure. The excess
ad impressions in that case might be sold on a spot market when it
becomes clear that the ad impressions are available, e.g., immediately
before the ad impressions might be used.

[0041] According to one aspect, the two markets are to be merged insofar
as possible, for ad impressions sold under guaranteed contracts and ad
impressions sold only when they prove to be available. This is
accomplished in part by optimizing the selected proportion of ad
impressions that are committed to guaranteed delivery contracts versus
the proportion that are sold if possible when excess ad impressions prove
to be available. This is also accomplished in part by providing
competition between guaranteed and non-guaranteed demand when
accomplishing the delivery of emerging ad impressions as the ad
impressions become available. These steps are accomplished by the ad
distribution system 250 as an intermediary between the advertisers 200
and media outlets 230.

[0042] The ad distribution system 250 functions to optimize the
proportions or the division of ad impressions that are allocated to
guaranteed contracts or to ad hoc spot sales. The optimization can be
accomplished during negotiations as to whether to commit to guaranteed
sales, but may also be accomplished repetitively by the ad distribution
system 250 as conditions change over time. In addition to negotiations in
advance, the ad distribution system 250 matches demand increments of the
representative profiles of advertiser versus projected probable
opportunities to use ad impressions, and repetitively updates the
projections and rebalances the proportions that are used or planned for
use either to satisfy guaranteed obligations or to be sold on the ad hoc
spot market. The ad impressions are allocated as a function of price and
performance, namely to achieve: (1) a high likelihood that guaranteed
obligations are met, (2) a close match of allocated ad impressions with
the representative profiles of the advertisers, and (3) allocation of the
ad impressions to the users that achieve the highest revenue for the
media outlets.

[0043] FIGS. 3 and 4 are block diagrams illustrating exemplary
architectures for advertising delivery systems 300. In FIG. 3, the
advertising delivery system 300 is configured to integrate handling of
determined commitments to provide ad impressions together with emergent
opportunities to make spot sales. The determined commitments are
generally termed "guaranteed" contracts or guaranteed delivery (GD) of ad
impressions, but the idea of guaranteed contracts encompasses any
commitment entered before the moment of allocation of an ad impression to
a demand, when the allocation reduces the total supply of remaining ad
impressions that are available, and thus reduces the number of ad
impressions that might yet be committed to another guaranteed contract or
might be allocated for spot sales up to the last possible moment.

[0044] The advertising delivery system 300 can be embodied as a service of
a programmed network server that manages the allocation of the supply of
ad impressions available from subscribing website operators and similar
media outlets versus the demand by advertisers to use the ad impressions,
optionally providing the interface through which ad content is routed to
the media outlets for insertion, as windows, banners and other elements
of web pages being composed for display by the respective browser
programs that compose the web pages for viewing by users, e.g., when
surfing the Worldwide Web. In an advantageous embodiment supported by
user interfaces for the advertisers and media distributors or outlets,
the system, including methods, can be configured to manage allocation of
guaranteed-delivery ad impressions in a number projected by media
distributors to be available, and also to manage the offering and ad hoc
sale of excess ad impressions that are found to be available beyond those
that were projected. These excess impressions can be sold at auction and
used up to the time at which it becomes apparent that the number of
impressions in the actual supply exceed what was projected. Alternatively
or in addition, impressions to be sold at auction do not have to be
excess impressions. For example, if the projected auction price is higher
than the under-delivery penalty cost, it may be more profitable to sell
the impressions at auction, whether or not they are in excess to the
projected guaranteed ad impressions.

[0045] The advertising delivery system 300 in FIG. 3 can unify the
allocation and sale of ads, eliminating artificial separation between the
ad impression inventory that is sold months in advance under agreements
entailing guaranteed delivery (i.e., obligations as to the number and
nature of impressions and potential penalties for inability to deliver)
versus the remaining inventory, normally from overly-conservative
estimates and projections, to be sold using a real-time auction, spot
market or terms of "best efforts" non-guaranteed delivery (NGD).

[0046] The advertising delivery system 300 manages advertising to serve
contracts (i.e., guaranteed ad impression deliveries) and non-guaranteed
(NGD) contracts. As a result, high-quality or most sought after
impressions are allocated to the guaranteed contracts and non-guaranteed
contracts. This mode for ad impression allocation may realize the full
potential of the additional ad impressions that are available when the
number of ad impressions proves to be greater than the number that was
projected. By automated allocation and management of non-guaranteed
delivery impressions, including allocation and contractual commitment of
ad impressions immediately prior to the time that the impressions become
available, a mix of guaranteed and also non-guaranteed contracts can form
a unified marketplace whereby an impression can be allocated to a
guaranteed or non-guaranteed contract efficiently, based on the value of
the impression to the different contracts, and with less value risked on
the ability to project ad impression availability far in advance. A
unified marketplace for long term (guaranteed) impressions and short term
ones as well, enables equitable allocation of ad impression inventory,
and promotes increased competition between guaranteed and non-guaranteed
contracts.

[0047] One aspect of the ad delivery system 300 is a bidding mechanism
that enables guaranteed contracts to bid on the spot-market for each
impression and compete directly with non-guaranteed contracts, while
still meeting the guaranteed goals for the contracts. This competition is
facilitated if the value of ad impressions on the spot market is subject
to highly refined targeting. For example, a selection of ad impressions
targeted to "one million Yahoo! Finance users from 1 Aug. 2008-31 Aug.
2008" is diluted and potentially less valuable to certain advertisers
compared to "100,000 Yahoo! Finance users from 1 Aug. 2008-8 Aug. 2008
who are males between the ages of 20-35 located in California, who work
in the healthcare industry and have recently accessed information on
sports and autos."

[0048] In order to shift to refined targeting, the advertising industry
needs to forecast future ad impression inventory to a fine-grained level
of targeting, e.g., numerous variables with tight ranges or close
adherence to examples. Advantageously, correlations between different
targeting attributes are identified and exploited by producing correlated
variable values that can be compared directly to match ad impressions
with demand. Taken to a very fine level, it may be appropriate to manage
contention in a high-dimensional targeting space with hundreds to
thousands of targeting attributes because different advertisers can
specify different overlapping targeting combinations. If numerous
targeting combinations are accepted and guaranteed, the advertising
delivery system 300 may help ensure that sufficient inventory is
available, while minimizing a supply cost associated with an increase in
ad impressions.

[0049] In FIG. 3, the advertising delivery system 300 coordinates the
execution of various system components, operating as a server with
several subsystems devoted to arranging for handling the contractual
matching of guaranteed ad impressions allocated to demands according to
projections, plus spot market sales of ad impressions that become
available, and serving ads to fill the ad impressions.

[0050] An admission control and pricing sub-system 302 facilitates
guaranteed ad contracts, preferably for a time period up to a year in
advance of actual presentation of ad impressions that are contracted.
This sub-system 302 assists in pricing guaranteed contracts, and is
coupled to supply and demand forecasting subsystems for this purpose.

[0051] An ad serving sub-system 304 has a subsystem that matches ad
guarantees (demands) with opportunities (ad impressions), including
serving the guaranteed impressions and also through ad hoc bidding system
whereby selected guaranteed impressions may be supplied by deals on the
spot market at favorable terms.

[0052] The admission control module 302 has input and output signal paths
for interacting with sales persons who negotiate and contract with
advertisers. A sales person may issue a query that defines a specified
target (e.g., "Yahoo! finance users who are California males who like
sports and autos") and the Admission Control module determines and
reports the available inventory of ad impressions for the target and the
associated price. The sales person can then book a contract accordingly.

[0053] The ad server module 304 takes on an ad impression opportunity,
which comprises a user such as a web page viewer and a context, such as a
URL for the visited page and information on the theme of the content of
the web page begin viewed. Other information useful for targeting may be
available, such as the succession of URLs visited by the user prior to
the visited page. The ad server module 304 returns a guaranteed ad to
fill the ad impression opportunity, and determines an amount that the
system is willing to bid for that opportunity in the spot market (an ad
exchange 306).

[0054] The operation of the ad delivery system 300 is orchestrated by an
optimization module 310. This module periodically takes into account a
forecast of supply (future impressions that are projected), future
guaranteed demand (projected guaranteed contracts) and non-guaranteed
demand (expected bids in the spot market) that are generated from a
supply forecasting module 313, and two demand forecasting modules 315,
317 that are arranged to distinguish between guaranteed and
non-guaranteed demand elements. However, as ad impressions are made
available, the system can decide whether to use the ad impression to
satisfy the guaranteed commitments or to apply them to the spot market.

[0055] The optimization module 310 matches supply to demand using an
overall objective function as described herein, namely matching instances
of ad impressions (supply) to meet instances of demand according to the
advertisers' representative profiles of demand, preferably using a norm
function that matches supply and demand according to the distance between
the variable values of the supply and demand instance attributes in
multi-dimensional space. The optimization module 310 sends a summary plan
characterizing the optimization results to the admission control and
pricing module 302 and to a plan distribution and statistics gathering
module 312. The plan distribution and statistics gathering module 312
sends information defining the plan to the ad servers 304. The plan
produced by the optimization module can be updated periodically as
estimates for supply, demand, and delivered impressions are available,
e.g., every few hours.

[0056] Given the plan, the admission control and pricing module 302 works
as follows. When a sales person issues a targeting query for some
duration in the future, the system first invokes the supply forecasting
module 313 to identify how much inventory is available for that target
and duration. As mentioned earlier, targeting queries can be very
fine-grained, thus having numerous values in a multi-dimensional space
having numerous coordinate axes. The supply forecasting module uses a
scalable multi-dimensional database indexing technique for this purpose,
with bit-map indices, to enable correlations between different targeting
attributes so that the values of instances of supply and demand have some
coordinate axes in common, and so that where values are unknown, a
statistical probability may be available either to infer a likely value
or to dictate that the representative profile should entail distributing
supply or demand instances over a range of values for a coordinate axis.

[0057] Generating values on the coordinate axes for supply and demand
instances is only a part of the larger problem of allocating supply and
demand because there is contention between alternative demands for the
same instance of supply and vice versa. For example, if there are two
demand contracts: "Yahoo! finance users who are California males" and
"Yahoo! users who are aged 20-35 and interested in sports," it may be
advantageous to take into account the correlation between the demand
instances to avoid double-counting, in this example because male
California finance users may have a high correlation with that age
bracket and with an interest in sports.

[0058] In order to deal with this contention problem in a high-dimensional
space, a supply forecasting system preferably computes the match between
supply instances as impression samples as opposed to a raw count of
available ad impressions. The samples of impressions are used as inputs
to compute whether multiple demand contracts are connected to the
attributes of a given impression.

[0059] Given the impression samples, the admission control module 302 uses
the plan communicated by the optimization module 312 to calculate the
contention between contracts in the high-dimensional space, and returns
an available inventory measure to the sales persons without
double-counting. In addition, the admission control module 302 calculates
a proposed price for each contract and returns that along with the
quantity of available impressions.

[0060] Given the plan, the ad server module 304 works as follows. When an
opportunity is presented, for example because a user's browser is engaged
in generating the display of a web page from HTML data and encounters a
graphic that is linked to a web address associated with the ad server, an
IP call is made for associated media content (e.g., text, graphics,
animation, etc.). The ad server module 304 calculates the contention
among contracts for this impression in a manner similar to what is done
by the admission control and pricing module 302 when determining
contractual terms beforehand. Given this instance of an available ad
impression, and with contention information and knowledge about
non-guaranteed demands, the ad server module 304 responds by selecting a
contract with an instance to be filled. The ad server module 304
generates a bid that serves to evaluate the contract, and sends
information on the contract and the bid to the exchange element 306. It
is then possible for the exchange to associate an instance of a
non-guaranteed contract, e.g., to sell the ad impression rather than to
fill it in satisfaction of the guaranteed contract that is in hand. If
the ad impression is sold, the ad server can return content provided from
the buyer through the exchange module 306. If the terms available over
the exchange are less favorable, the ad server 304 returns the content
associated with the guaranteed demand instance.

[0061] Matching a given set of contracts--representative profile demands
having values associated with various variables--versus and a set of
impression samples--ad impression instances of supply, also having values
associated with various values--is a core task, and is served
substantially by the optimization module 310. The task is to decide how
to allocate the projected or available ad impressions to satisfy the
specifications of the demand contracts. One of the goals is a
representative allocation. When a contract demand might be satisfied by
multiple eligible ad impression types, each of which would contribute in
some degree to meeting the demand, it is desirable to allocate some
volume of each eligible impression type to corresponding contract
demands. In short, it is desirable to allocate supply instances that have
a given set of attribute values, to favor targets who have matched
attribute values, but not to allocate all the supply to targets based on
one attribute value at the expense of others. It is desirable to spread
the allocation volume in a manner that is related to the number of
instances of all impression types and demand instances, for example
proportionately. Advantageously, the allocation favors but does not serve
exclusively, those matches wherein certain variable attributes are close
in value (i.e., the viewer in context closely meets one of several
measures targeted) at the expense of other attributes.

[0062] In the unified marketplace, there are two competing sources of
demand to which a particular ad impression might be allocated. An ad
impression might be used to satisfy a guaranteed delivery (GD) obligation
under a contract, or might be sold on the non-guaranteed delivery (NGD)
spot market. In a market that is not unified, and assuming that there was
sufficient demand from advertisers at the prices offered, the ad
impressions of the media distributors might be contractually guaranteed
only insofar as their projected availability has a high level of
confidence. It is an aspect of the present technique, however, not to
allocate only on confidence in availability but instead to seek to
maximize the efficiency and value of the allocation to both portions of
the demand. Accordingly, the optimization module is used to seek an
efficient division of allocations between the guaranteed and spot
markets.

[0063] When impressions are allocated to guaranteed delivery contracts,
the representativeness of the allocation is the major goal, namely to
closely match the allocation to the number and type of ad impressions
that define the representative profiles of the advertisers. On the other
hand, when ad impressions are sold on the non-guaranteed delivery spot
market, the goal is merely revenue.

[0064] The total available ad impressions are a finite supply. If an
impression is allocated to guaranteed delivery, that impression is not
available for non-guaranteed demand, and vice versa. The marginal revenue
that might be obtained from sale of an ad impression on the spot market
therefore is compared directly with the marginal value of using that same
ad impression to satisfy a guaranteed delivery obligation. As explained
herein, this gives a basis in which to make reasoned decisions as to what
proportion of available ad impressions should most efficiently be devoted
to meeting guaranteed delivery obligations and what proportion should be
sold on the ad hoc spot market, for example at auction. Such decisions
are enabled in the optimization module 310.

[0065] The marginal revenue from a spot market sale of an ad impression is
a lost opportunity that is comparable to a cost for a guaranteed delivery
allocation of that ad impression. One task of the optimization module 310
is to decide how to balance the allocations between guaranteed delivery
contracts and the non-guaranteed delivery spot market to achieve
efficiency and other business goals. Another task of the optimization
module 310 is to minimize a supply cost associated with a changeable
number of ad impressions.

[0066] The question of whether to allocate to guaranteed or non-guaranteed
allocations is regarded herein as a multi-objective optimization problem
with the number and marginal revenue of both allocation categories
contributing to a common total but their respective contributions
competing for the available supply. Both guaranteed delivery value (which
equates to representativeness) and non-guaranteed delivery market revenue
(which as an opportunity cost can be assessed against guaranteed delivery
value) are modeled explicitly as described herein. Modeling in this way
provides a framework to test the results of different functions for
evaluating representativeness, enabling the model to identify a
corresponding efficient allocation between guaranteed and spot market
allocations. The model effectively provides business controls that when
imposed on a mathematical optimization that produces a trajectory or
range of potential control points, establishes one point in the range to
be used as the basis of control. This result accrues using a methodology
that establishes a monetary value equivalent to the value of
representativeness, for use in solving a multi-objective optimization
problem.

[0067] FIG. 4 is a block diagram of an alternate architecture for the
advertising delivery system 300. An optimizer 310 utilizes inputs from
the supply forecasting module 313, the guaranteed demand forecasting
module 315, and the non-guaranteed demand forecasting modules 317. Supply
forecasting 313 provides forecast ad opportunities (impressions) from
which the optimizer 310 may determine a cost associated with a supply
volume of the ad impressions, which will be discussed in more detail
later. Guaranteed demand forecasting 315 provides forecast contracts and
non-guaranteed demand forecasting 317 provides forecast non-guaranteed
demand prices. The optimizer 310 uses the inputs to run optimization,
such as described with regard to the algorithms below and with reference
to FIG. 3, to generate an ad allocation plan.

[0068] The optimizer 310 allocates the plans to both admission control 302
and the ad server 304. The admission control 302 uses the allocation plan
to calculate inventory level to decide whether or not to accept a booking
query. The ad server 304 uses the allocation plan to decide whether to
allocate an incoming ad opportunity to serve a guaranteed contract or
sell the ad opportunity to the non-guaranteed marketplace. The ad server
304 serves an advertisement to an application 125A-M, such as a browser,
of user 120A-N in accordance with the plan. The optimizer 310 executes
allocation optimization algorithms periodically, such as when inputs are
updated with all the forecasts as well as feedback of newly-booked
contracts from the admission control 302 and advertisement delivery
statistics from the ad server 304.

[0069] Algorithms, such as those used by the optimizer 310 of the system
300, are modeled mathematically and shown graphically using the variables
listed and defined in Table I:

[0071] There are three objectives in this model, where f1(z) is the
under-delivery penalty cost, f2(y) is the negative NGD revenue where
minimizing f2(y) is equivalent to maximizing the NGD revenue, and
f3(x;θ) is the representativeness of allocation for GD
contracts. These constraints describe the basic network flow of the
optimization model, e.g., for each supply node, the total allocation to
both GD contracts and NGD market must equal the supply volume; and for
each demand node, the total allocation from all eligible supply nodes
plus the under-delivery volume (if any) must equal the demand volume.

[0072] When the supply of ad impressions is changeable, the inflated
supply volume is associated with a cost. Assume that the cost of supply i
can be modeled by a piece-wise linear convex function as shown in FIG. 5,
where

0=mi0≦mil≦ . . .
≦bi,Li+1=∞ (8)

are the break points,

0=qi0≦qil≦ . . . ≦biLi (9)

are the costs associated with each segment of the cost function and

si=(si0, . . . ,siLi) (10)

is the variable of the supply volume. Note that the break point mil
represents the threshold of the supply volume, below which the cost of
supply is zero. Any supply volume above the threshold is associated with
a cost. Adding this cost as f4(s) into the multi-objective
allocation model of equation (1), the model may be recast as:

[0080] The four objectives may be interrelated such that to achieve
maximum NGD revenue by serving ad impressions to the NGD market, GD
representativeness may decrease. Likewise, to serve ad impressions to
maximize GD representativeness, that may impact NGD revenue. There may
also be tradeoffs between under-delivery penalties and NGD revenue. If
enough ad impressions do not exist to satisfy all the GD contracts, an
under-delivery penalty may apply. In the alternative, or in addition, the
number advertisement impressions may be increased to satisfy all the GD
contracts while minimizing a cost as a result of making such a change to
the number of ad impressions.

[0081] Furthermore, an added revenue from serving an ad impression to the
NGD market instead of the GD market may be greater than the
under-delivery penalty, which may be acceptable if a volume of the ad
impressions cannot be increased due to minimization of the cost of
changing supply volume. The system may also buy ad impressions from the
market. In this case, the system may minimize the NGD cost. The system
may also allow for over-delivery to the market. The following approach
may be used to help determine whether to serve a particular ad impression
to an NGD market or a GD market, and which GD market to serve it to. The
following may also be used to minimize a cost of changing the supply of
ad impressions, and therefore balance the benefits of being able to
deliver more ad impressions to both the GD and NGD markets while avoiding
loss of advertiser revenues due to decreasing ad impression quality.

[0082] A solution for a multi-objective program may be referred to as
efficient if there is no other solution that is equal or better in all
objectives and is better in at least one objective. All the efficient
solutions constitute the efficient frontier. Solving the multi-objective
optimization problem seeks to find a solution on the efficient frontier
corresponding to specified priority or preference between the objectives.
There are different approaches to the problem to get an efficient
solution.

[0083] One approach is to optimize the weighted sum of objectives:

min w1f1(z)+w2f2(y)+w3f3(x;θ)+w4f4(s) (18)

s.t. Σj|iεBjxij+yi=Σlmilsil.A-inverted.i (19)

ΣiεBjxij+Σkzjk=dj.A-in-
verted.j (20)

xij≧0.A-inverted.i,j (21)

yi≦0.A-inverted.i (22)

0≦zjk≦bjk-bj,k-1.A-inverted.j,k (23)

0≦sil≦mi,l+1-mi,l.A-inverted.i,l (24)

[0084] where w=(w1, w2, w3,
w4)≧0,Σ14w1=1 are the weights for the
relative priority of the objectives. A higher prioritized objective
should be solved first. It may, however, be difficult to set the weights
to truly represent the priority. For example, to set the right weight,
the system needs to determine how much one unit of representativeness is
worth in terms of a dollar amount. Although the under-delivery penalty
f1(z), the NGD revenue f2(y) and supply cost f4(s) may be
in terms of monetary values and thus easily compared to each other, the
representativeness utility f3(x;θ) has abstract mathematical
meaning (distance to the proportional allocation), and it is thus not
straightforward to compare the representativeness with the other
objectives.

[0085] Another approach to multi-objective optimization is via goal
programming. The system may solve each objective one by one, by adding
constraints on the values of one or more previously-solved objectives,
which are also referred to herein as requirements. No specific order is
necessary as the below steps may be taken in different orders so long as
at least one requirement determined from solving another objective
includes a new constraint. Accordingly, the below is but one example of a
possible sequence of steps in the goal programming algorithm. Imposing a
new constraint on a value of a requirement may also be referred to as
relaxing the earlier-solved requirement to allow for less than an optimum
value for that requirement. Such relaxing earlier-solved requirements may
result in a more balanced allocation plan in terms of delivering ad
impressions between GD contracts and in the NGD market.

[0086] Step 1: Minimize Under-Delivery Penalty

min f1(z)=ΣjΣkcjkzjk (25)

s.t. Σj|iεBjxij+yi=Σlmilsil.A-inverted.i (26)

ΣiεBjxij+Σkzjk=dj.A-in-
verted.j (27)

0≦zjk≦bjk-bj,k-1.A-inverted.j,k (28)

0≦sil≦mi,l+1-mi,l.A-inverted.i,l (29)

[0087] No extra constraints are added in this step or any of the steps if
executed first in the sequential order of algorithmic steps.

[0088] Step 2: Minimize NGD Cost (Maximize NGD Revenue)

min f2(y)=-Σiriyi (30)

s.t. Σj|iεBjxij+yi=Σlmilsil.A-inverted.i (31)

ΣiεBjxij+Σkzjk=dj.A-in-
verted.j (32)

f1(z)≦(1+η1)f1* (33)

yi≧0.A-inverted.i (34)

0≦zjk≦bjk-bj,k-1.A-inverted.j,k (35)

0≦sil≦mi,l+1-mi,l.A-inverted.i,l (36)

[0089] where f1* is the optimum objective value of total
under-delivery penalty cost from the first step and η1≧0
is the percentage of f1* to relax. For example, if the optimum
(minimum) under-delivery penalty cost is $1,000 and η1=0.9, the
model can afford the under-delivery penalty cost up to $1,100 so as to
maximize the NGD revenue.

[0090] Step 3: Minimize Ad Impression Supply Cost

min f4(s)=ΣiΣlmilsil (37)

s.t. Σj|iεBjxij+yi=Σlmilsil.A-inverted.i (38)

ΣiεBjxij+Σkzjk=dj.A-in-
verted.j (39)

f1(z)≦(1+η1)f1* (40)

f2(y)≦(1+η2)f2* (41)

yi≧0.A-inverted.i (42)

0≦zjk≦bjk-bj,k-1.A-inverted.j,k (43)

0≦sil≦mi,l+1-mi,l.A-inverted.i,l (44)

[0091] where f1* and f2* are the optimum objective values of
total under-delivery penalty cost and NGD revenue from the last two
steps, respectively, and η1≧0 and η2≧0
are the percentages of f1* and f2* to relax, respectively. For
example, if the optimum (minimum) under-delivery penalty cost is $1,000,
the optimum (maximum) NGD revenue is $1,000,000, where η1=0.9,
and η2=0.9, the model can afford the maximum under-delivery
penalty cost of $1,100 and the minimum NGD revenue of $900,000 so as to
minimize the ad impression supply cost.

[0092] Step 4: Maximize GD Representativeness

min f3(x;θ) (45)

s.t. Σj|iεBjxij+yi=Σlmilsil.A-inverted.i (46)

ΣiεBjxij+Σkzjk=dj.A-in-
verted.j (47)

f1(z)≦(1+η1)f1* (48)

f2(y)≦(1+η2)f2* (49)

f4(s)≦(1+η4)f4* (50)

xij≧0.A-inverted.i,j (51)

yi≧0.A-inverted.i (52)

0≦zjk≦bjk-bj,k-1.A-inverted.j,k (53)

0≦sil≦mi,l+1-mi,l.A-inverted.i,l (54)

[0093] where f1*, f2* and f4* are the optimum objective
values of total under-delivery penalty cost, NGD revenue and supply cost
from the last three steps, respectively, and η1≧0,
η2≧0, and η4≧0 are the percentages of
f1*, f2*, and f4* to relax, respectively. For example, if
the optimum (minimum) under-delivery penalty cost is $1,000, the optimum
(maximum) NGD revenue is $1,000,000, the optimum (minimum) supply cost is
$2,000, where η1=0.9, η2=0.9, η4=0.95, the
model can afford the maximum under-delivery penalty cost of $1,100, the
minimum NGD revenue of $900,000, and the maximum supply cost of $2,100 so
as to maximize the GD representativeness.

[0094] When the under-delivery penalty f1(z), the NGD revenue
f2(y), and f4(s) are all of monetary values, it is meaningful
to combine them using weighted sum. The optimal monetary objective value
of the combined function can then be used as a guideline for the
non-monetary objective of representativeness.

[0095] Step 1: Optimize Monetary Objectives

min f124(y,z,s)=w1ΣjΣkcjkzjk-w-
2Σiriyi+w4ΣiΣlmi-
lsil (55)

s.t. Σj|iεBjxij+yi=Σlmilsil.A-inverted.i (56)

ΣiεBjxij+Σkzjk=dj.A-in-
verted.j (57)

yi≧0.A-inverted.i (58)

0≦zjk≦bjk-bj,k-1.A-inverted.j,k (59)

0≦sil≦mi,l+1-mi,l.A-inverted.i,l (60)

[0096] Step 2: Optimize Non-Monetary Objectives

min f3(x;θ) (61)

s.t. Σj|iεBjxij+yi=Σlmilsil.A-inverted.i (62)

ΣiεBjxij+Σkzjk=dj.A-in-
verted.j (63)

f124(y,z,s)≦(1+η124)f124* (64)

xij≧0.A-inverted.i,j (65)

yi≧0.A-inverted.i (66)

0≦zjk≦bjk-bj,k-1.A-inverted.j,k (67)

0≦sil≦mi,l+1-mi,l.A-inverted.i,l (68)

[0097] where f124* is the optimal objective value of monetary cost
from the first step and η12≧0 are the percentages of
f124* to relax.

[0098] The supply cost function f4(s) above is assumed to be
piece-wise linear and convex, which enables the minimization of supply
cost to be modeled as continuous linear programming (LP). In general, the
supply cost function may not be convex, in which case integer variables
would have to be introduced, resulting in a mixed integer problem that is
hard to solve for a large-scale model.

[0099] Note that the supply volume si=(si0, . . . ,
siLi) is the variable of supply volume. Since the cost
associated with the first segment of the cost function, qi0=0, the
maximization of NGD revenue will force the solution si0=mi1.
The solution of the remaining elements of si will depend on the
trade-off among the objectives. However, the convexity assumption of the
supply cost function will guarantee that

sil<mi,l+1-mi,lsi,l+1=0. (69)

[0100] The advertising distribution system as disclosed mediates and
distributes advertising opportunities, especially insertions of ads on
web pages, according to representative targeting profiles of advertisers.
The number and characteristics of future ad impressions is forecast. A
portion is allocated to guaranteed-delivery advertiser contracts and the
remainder is offered on a spot market. A division between guaranteed and
spot market allocations is sought to maximize revenue, taking into
account a value associated with meeting the representative profiles of
advertisers and the quality of the ad impressions available for delivery.
The value of representativeness can be inferred from the marginal revenue
of a spot market sale, and optionally weighted.

[0101] The techniques as described are not limited to an Internet based
advertising distribution system and can be applied to other instances
where there is a need to allocate supply and demand while delivering
value in exchange for revenue wherein the demand increments fall into
categories having at least one of quantities and revenues that differ
between the categories. Inasmuch there are totals of quantity and
revenue, it is known that an allocation to one category reduces the
allocation to the other category. A relationship can be projected as
described that demonstrates the quantities and revenues that result from
allocating the total supply more or less to one or the other of the at
least two distinct categories, from zero to 100% or at least fro/m zero
to a maximum proportion of the total supply. What remains is to determine
the operating point.

[0102] One or more goals may be imposed on the relationship in addition to
accounting for distribution of all the supply to one or the other of the
allocation categories. The goal helps to determine a point in the
relationship curve that corresponds to a particular proportionate
allocation. The supply increments are then allocated to the demand
increments at this particular proportion in at least one of a planned
allocation and an actual allocation including delivering the supply
increments. This allocation can be used when planning the proportion of
projected ad impressions devoted to guaranteed delivery contracts, or can
be used when deciding how to use the successive ad impressions that prove
to be available, for example when web page hits occur enabling the
transmission of ad copy for insertion into the web page as rendered.

[0103] The disclosed allocation technique can incorporate functions that
calculate the value of representativeness so as to rate the extent to
which emerging ads meet advertiser representativeness specifications,
e.g., functions that allow a comparison of ad impression characteristics
and advertiser specifications as a measure of quality. Alternatively, the
allocation can be based on an inferred monetary value based on the
opportunity cost of employing an ad impression to meet a guaranteed
demand. The opportunity cost is at least equal to the amount that the ad
impression would bring in on the spot market. It is advantageous,
however, to weight the importance of representativeness versus revenue,
preferably to assume that a high degree of representativeness (high ad
quality from the viewpoint of the advertiser) is a desired aspect for the
ad distribution service to deliver. Weighting can be accomplished by a
factor that favor representativeness or by choosing a proportion of
revenue that should be attributable to representativeness, and thus
contributes to long term customer goodwill.

[0104] FIG. 8 is a flow chart of a method for distributing advertisement
impressions through an exchange in which the number of ad impressions is
changeable. At block 400, an ad delivery or distribution system
establishes a relationship between delivery of ad impressions to
guaranteed (GD) contract demand and to non-guaranteed (NGD) demand on an
advertisement spot market, such as through an ad exchange auction. The
relationship defines a range of possible proportions of allocation of the
ad impressions between GD and NGD demand. At block 410, the system
imposes one or more objectives on the relationship between allocation of
ad impressions between GD and NGD demand. The objectives may include one
or more of: (1) minimizing a supply cost of the ad impressions; (2)
maximizing NGD demand revenue; (3) minimizing under-delivery penalties;
and (4) maximizing guaranteed (GD) demand representativeness. Other
objectives are envisioned.

[0105] To minimize the supply cost, the system may moderate an increase in
the number of ad impressions available for allocation, to minimize a cost
associated with reducing a quality of the ad impressions as their volume
increases. By way of implementation, the number of ad impressions may be
moderated when the ad impressions change based on one or more events.
These events may include, but are not limited, to: (1) changing a score
threshold for qualifying a user into a specified interest category such
as for behavioral targeting (BT) of users; (2) changing navigational
links on a web page; and (3) dynamically changing displayed content on a
web page. Other events are envisioned.

[0106] Ad distribution may be optimized through goal programming. At block
420, the system solves for a first of the objectives to generate a first
requirement. At block 430, the system may relax the first requirement
while solving for a second of the objectives to generate a second
requirement. The second requirement therefore is affected by relaxing the
first requirement. To relax a requirement may be viewed as the system
allowing departure from its solved-for optimum value. Accordingly,
relaxing a requirement that maximizes the objective is to allow the
solved-for requirement to be less than the maximum value. In contrast,
relaxing a requirement that minimizes the objective is to allow the
solved-for requirement to be more than the minimum value.

[0107] At block 440, the system may relax the first and/or the second
requirements while solving for a third of the objectives to generate a
third requirement. The third requirement is therefore affected by
relaxing the first and/or the second requirements. At block 450, the
system may relax any one of the first, second, and/or third requirements
while solving for a fourth of the objectives to generate a forth
requirement. At block 460, the system may take the solved-for
requirements from block 430, block 440, or block 450 to generate an
allocation plan to control serving the ad impressions according to the
range of possible proportions of allocations between the GD contract
demand and the NGD demand on the spot market. The proportions of
allocations may range anywhere from zero to 100%. The system may execute
the method of FIG. 8 through an optimizer executing instructions stored
in memory of a server. The system may also allow prioritization of the
objectives and therefore solve for the objectives to generate the
allocation plan in order of the prioritization.

[0108] This disclosure encompasses methods, systems for practicing the
methods, programmable data processing apparatus and/or program data
carriers that store code enabling a general purpose computer to practice
the subject matter when coupled in data communication with sources of
advertiser information, sources of media distributor information, and
advertising copy that can be inserted when opportunities are reported by
the media distributors.

[0109]FIG. 9 illustrates a practical embodiment as a block level diagram
wherein the ad distribution system is configured as a computer system 750
that is coupled for data communications, for example to provide media in
the form of HTML web pages and graphics files over a communication path
traversing the Internet 755 to various remote users 757, who may be
appropriate targets for advertising content provided by advertisers 200.
The computer system 750 can be associated with a service such as a
directory service or search engine, or a retail or wholesale outlet or
any of various operations whose activities include transmission of media
to users 757.

[0110] The system 750 as shown can include one or more processors 772,
implemented using a general or special purpose processing engine such as
a microprocessor, controller or other control logic configuration. In the
example shown, processor 772 is coupled via a bus 780 to program and data
memory 774, an interface 776 for input/output with a local operator,
including, for example, a keyboard, mouse, display, etc., and a
communications interface 778. The communications interface is generally
shown coupled for communications with advertisers 200 or over the
Internet with remote users 757; however it is likewise possible that
other specific techniques could be employed to deliver data from the
advertiser to system 750, such as hand transferred data carriers,
telephone discussions or even paper exchanges. The manner of transmitting
media to the users 757 likewise is not limited to web page data
transmission and could comprise, for example, cable or other video
program distribution among other possible embodiments.

[0111] The memory 774 of the computing system advantageously includes
random access volatile memory and ROM, disc or flash nonvolatile memory
for initialization. The program instructions are stored in and executed
from the program memory to carry out the functions discussed above. The
memory can include persistent data storage for accumulated data
respecting advertiser and user information, for example on hard drives.
Advantageously, the memory 774 of system 750 can contain locally stored
versions of advertising copy that is to be inserted, especially for
servicing guaranteed demand. The memory 774 also can receive, preferably
store and insert at least some advertising copy from advertisers 22 who
undertake to use ad impressions obtained on the ad hoc spot market.

[0112] Alternatively or in addition, at least part of the advertising copy
to be inserted can be stored remotely and accessed by providing to the
browser at the user system the appropriate URLs identifying advertising
content to be inserted. For example, the system 750 can store and submit
to the user browser a network address for graphics or other content to be
inserted, which address refers to a system at or associated with the
advertiser 200, which system is coupled for web communications and is
configured to respond to an IP request for addressed graphic or media
content. That content can be obtained by bidirectional IP communications
between the browser and the system where the content is stored.

[0113] The persistent storage devices of memory 774 may include, for
example, a media drive and a storage interface for video or other
substantial storage capacity needs. The media drive can include a drive
or other mechanism to support a storage media. For example, a hard disk
drive, a floppy disk drive, a magnetic tape drive, an optical disk drive,
a CD or DVD drive (R or RW), or other removable or fixed media drive may
be employed. The storage media can include, for example, a hard disk, a
floppy disk, magnetic tape, optical disk, a CD or DVD, or other fixed or
removable medium that is read by and written to by the media drive.

[0114] The terms "computer program medium" and "computer useable medium"
and the like are used generally to refer to media such as, for example,
memory 774, various storage devices, a hard disk and hard disk drive and
the like. These and other various forms of computer useable media may be
involved in carrying one or more sequences of one or more instructions to
the processor 772 for execution. Such instructions, generally referred to
as "computer program code" (which may be grouped in the form of computer
programs or other groupings), when executed, enable the computing system
750 to perform features or functions of the embodiments discussed herein.

[0115] Alternatively or in addition, dedicated hardware implementations,
such as application specific integrated circuits, programmable logic
arrays and other hardware devices, may be constructed to implement one or
more of the methods described herein. Applications that may include the
apparatus and systems of various embodiments may broadly include a
variety of electronic and computer systems. One or more embodiments
described herein may implement functions using two or more specific
interconnected hardware modules or devices with related control and data
signals that may be communicated between and through the modules, or as
portions of an application-specific integrated circuit. Accordingly, the
present system may encompass software, firmware, and hardware
implementations.

[0116] The methods described herein may be implemented by software
programs executable by a computer system. Further, implementations may
include distributed processing, component/object distributed processing,
and parallel processing. Alternatively or in addition, virtual computer
system processing maybe constructed to implement one or more of the
methods or functionality as described herein.

[0117] The network could be the Worldwide Web and the advertising copy
could comprise banner ads, graphics in fields of specific size and
placement, overlaid moving pictures or animation, redirection to a
different URL, etc. The same targeting abilities are also applicable to
networks that are interactive to a lesser degree, such as cable
television ad insertion, which might be done at a head end or at a hub,
or even from a subscriber-specific set top box.

[0118] Although components and functions are described that may be
implemented in particular embodiments with reference to particular
standards and protocols, the components and functions are not limited to
such standards and protocols. For example, standards for Internet and
other packet switched network transmission (e.g., TCP/IP, UDP/IP, HTML,
HTTP) represent examples of the state of the art. Such standards are
periodically superseded by faster or more efficient equivalents having
essentially the same functions. Accordingly, replacement standards and
protocols having the same or similar functions as those disclosed herein
are considered equivalents thereof.

[0119] The illustrations described herein are intended to provide a
general understanding of the structure of various embodiments. The
illustrations are not intended to serve as a complete description of all
of the elements and features of apparatus, processors, and systems that
utilize the structures or methods described herein. Many other
embodiments may be apparent to those of skill in the art upon reviewing
the disclosure. Other embodiments may be utilized and derived from the
disclosure, such that structural and logical substitutions and changes
may be made without departing from the scope of the disclosure.

[0120] While one may not conclude definitely that any given subject (user
or browser) responds favorably if exposed to information or advertising,
for example by purchasing an advertised product or service, one can
establish a set of variables to characterize members of a population, to
determine values for those variables that are most characteristic of
actual purchasers (and by implication to assess the quality of ad
targets). The statistical methods above may enable correlation of a set
of variable values with selected subsets of the population consistent
with purchasers. Statistical methods also enable correlation among the
variables themselves. The result is a set of criteria such as age,
gender, location, income range, education, family status, etc., and
various rules of thumb that attempt to use combinations of certain values
of these criteria to make conclusions about the characteristics and
buying preferences of customers.

[0121] Variably defined subsets of the population are thereby rated for
the likelihood that members of each subset becomes a purchaser if exposed
to advertising. The subsets of the population can be distinguished by the
extent to which members are correlated to an ideal target for an
advertising piece.

[0122] There are mathematical ways to correlate variable values that may
be known about the population of subjects with other variable values that
may not be known. There are also ways to infer information such as
descriptive and demographic details about subjects, based on the
subject's current activities, including the websites that a subject may
be visiting, the entertainment programs being viewed, the periodical
publications that the person reads, etc. If an advertiser is promoting a
product that is associated with the content of a website or a
publication, then advertising on the website or in the publication may be
more valuable to the advertiser than advertising elsewhere or randomly,
because the subjects who are exposed to the advertising are relatively
more highly correlated with likely purchasers than other subjects and are
more likely to actually see the advertising.

[0123] An advertiser typically does not have close access to an isolated
population of subjects who are all very highly correlated with an ideal
likely purchaser. Even if the advertiser had access to such a population,
the advertiser may not devote 100% of its advertising effort to that
population. The advertiser also may want to devote advertising efforts to
other populations that are perhaps not so highly correlated, but where
advertising still has a positive effect. For example, an advertiser may
seek to spread advertising expenditures over a wide range of subjects and
over a wide geographic area, while perhaps biasing its efforts toward
subjects who are or might be correlated with a hypothetical ideal
purchaser.

[0124] The advertiser may determine a profile of representative
advertising over which advertising expenditures shall be devoted. This
profile may be discussed with possible advertising outlets such as
advertising brokers, advertising services (including on-line services
such as that offered by Yahoo!), media outlets such as web page operators
and cable media distributors, print publishers and others similarly
situated. Negotiations may ensue on the basis that the party controlling
the ad impressions demands payment and competing advertisers who want to
use the ad impressions are willing to pay for the ad impressions in
amounts that related to the extent to which the ad impressions match the
advertisers' representative profiles of what the advertisers demand.
Matching the use of impressions to adhere to the representative aspects
sought by the advertiser may be an objective. Maintaining
"representativeness" may achieve long term value.

[0125] The market for advertising on Internet web pages is particularly
well developed because information is available to characterize the web
page users (the potentially targeted subjects). Infrastructure is in
place for changeably inserting ad graphics and moving pictures, such as
Internet browsers. Data from click streams and sometimes from locally
stored cookies can carry context and history information forward in time
as the user surfs through different pages. Internet service providers
make at least generalized information on subscribers available routinely,
such as the subscriber's zip code. These information sources enable
information to be collected to gauge the characteristics of users and
enable an advertiser to define a representative advertising allocation
for which the advertiser contracts.

[0126] Internet web page operators are also in a good situation for
collecting data about information distribution events, such as reporting
on the availability and use of ad impressions. Executed ad impressions
can be counted and reported with associated context information, time of
day, location of recipient and so forth. This information enables the
operators to forecast the number of impressions and the characteristics
of users that are likely to be available to receive impressions ready to
be allocated to those users at a future date and time. The information
allows up to the moment monitoring of use of the ad impressions for
reporting compliance with contractual obligations to distribute a given
number of ads of a given type in a given time window.

[0127] Advertisers contract with advertising distributors and advertising
services to make use of ad impressions that are available to the
distributor or service. The advertising distributor might be a website
operator or an advertising warehouse that in turn contracts with website
operators. Available impressions may be determined in number and with
respect to attributes that determine the value of the impressions to the
advertiser. The attributes include characteristics that enable the
advertiser to judge how representative the recipients of the impressions
will be, compared to likely purchasers and to the advertiser's desired
profile of ad distribution. The advertising distributor may agree to
distinguish among potential users to whom impressions are delivered, for
example by the attributes of the users or the web content that the users
view. This aspect may be written into the contract. The advertising
distributor may commit to delivering a given number of impressions to
users of defined characteristics or in a defined context over a given
time window at some point in the future.

[0128] The advertiser may contract with the advertising distributor to
deliver a stated number of ad impressions to a stated number of website
viewers having stated demographic or other properties that correspond
with the representativeness aspects dictated by an advertiser. There may
be alternative ways in which the website operator could meet its
obligations. As one example, if the agreement is to deliver impressions
to users in a certain age group, the website operator might devote a
large ratio of available impression opportunities at a time of day when
the on-line user population of the age group is low, or a smaller ratio
of available impression opportunities at a time of day when the
percentage of users in that age group is higher, and in either case get
the number of impressions needed to meet the contractual obligation.

[0129] The website operator or other advertising distributor has degrees
of freedom in which to operate but may need information to define the
variations in users by factors that matter, such as the correlation of
user age to time of day of on-line access, in the example of a time
discrimination aspect. There are various such correlations possible
between category ratings that are known or might be inferred.

[0130] In order to assess its ability to meet contractual obligations, the
advertising distributor projects an estimate of the number of users of
given characteristics at some future date and time when offering to sell
ad impressions to an advertising campaign manager negotiating for the
advertiser. If the seller of ad impressions (the advertising distributor)
guarantees that a certain number of ad impressions are executed to users
of given attributes, the seller may be bound to comply, subject to
possible contractual penalties.

[0131] A seller may decide to guarantee a number of available impressions
that are relatively sure to be available at the future data and time.
Then if an excess number of impressions actually become available for
execution at that time, the seller may seek to exploit them in sales
under short term contracts, in an ad hoc spot market or by auction that
could occur at any time up to the moment that an ad impression is used.
The impressions that were committed by contract according to prudent
projections made ahead of time can be deemed "guaranteed" impressions.
The remaining impressions are "excess" or "non-guaranteed" impressions
and may be sold on last minute terms or on "best efforts" commitments by
the ad distributor.

[0132] In existing markets for on-line advertising, the manner of sale and
the use of guaranteed and non-guaranteed ad impressions may be distinctly
different for the two types. Based on their confidence in projections of
ad availability, the seller of ad impressions may prefer to sell
guaranteed impressions and to develop long term relationships with
advertisers characterized by dependability in meeting obligations.
However, undue caution when making projections may leave saleable ads
unsold, or may affect the prices that quality ad impressions may command.
Furthermore, the ability to correlate user characteristics with ad
impressions accurately may be best immediately before the ads are used.
Therefore, some of the highest quality ads (namely those that are highly
correlated with some desired target category) arise only after it is too
late to handle them in guaranteed contracts. For these impressions, a
second marketplace is advantageous, apart from the marketplace in the
sale of projected future impressions under contracts that contain
obligations as to the number of impressions that provided. This second
marketplace is not based substantially on promises of future performance
and instead is based on exploiting currently available opportunities.

[0133] If the advertising distributor was cautious when negotiating
contracts to sell guaranteed impressions, the advertising distributor may
have reserved a substantial portion of the impressions that were
projected to become available, to avoid contractual penalties if the
projections prove too optimistic. These may be sold or else wasted.

[0134] If impressions become available that are matched to an advertiser's
representative profile, the impressions have a high value in advertising
effectiveness to that advertiser. These non-guaranteed impressions might
be sold at a high price. Assuming that some proportion of projected
impressions are to be reserved to ensure the ability to meet obligations,
a problem is presented in how optimally to allocate the impressions
between the guaranteed and non-guaranteed categories when planning and
negotiating contracts for use of projected future ad impressions.
Assuming that the decisions have been made, the situation may change when
projections are proved or disproved in reality. The above system and
method, including optimizer 310, may optimally allocate emergent supply
of ad impressions either to obligated/guaranteed impressions or to
non-guaranteed impressions, in a manner that is agile and quick.

[0135] The system and method may consider multiple objectives. The
advertising distributor meets his contractual obligations, and delivers
quality impressions to the advertiser in exchange for value received. The
advertising distributor's long term performance under these objectives,
including meeting contractual obligations for delivery of guaranteed
impressions, is important to maintaining mutually beneficial relations
between the advertising distributor and its customers, namely the
advertisers.

[0136] The advertising distributor may maximize revenues obtained in
exchange for use of the ad impressions that are available. Revenues can
be maximized when accurate projections can be made, including forecasting
the supply of impressions that are available, assessing the demand for
guaranteed impressions and forecasting the future demand in the event of
short notice ad hoc sales of excess impressions, by auction or otherwise.

[0137] The foregoing situation can be considered a confluence of
overlapping marketplaces. For each marketplace, the impressions
(information exposures) that are available according to projections, or
the impressions that actually prove to be available when the time
arrives, each represent a finite supply of information distribution
opportunities. These information distribution opportunities need to be
allocated to the demand for use of ad impression opportunities associated
with highly representative advertiser-targeted groups. The allocation may
maximize representativeness of ad impressions compared to the
advertiser's targeting, which comes from ensuring that guaranteed
impressions are faithfully delivered. The allocation may maximize the
revenue to the advertising distributor, who may be a media operator, by
ensuring that no impressions go unsold, or are sold at prices that are
less than the ad impressions should reasonably command.

[0138] A given number of impressions is projected to be available. If the
advertising distributor decides to use some number of the projected
impressions under guaranteed contracts, then the number available for ad
hoc auction is reduced, and vice versa. The above system and method may
provide an optimal and efficient technique to control the relative
allocations of guaranteed ad impressions under contracts versus
non-guaranteed ad impressions to be sold on the spot market.

[0139] The above disclosed subject matter is to be considered
illustrative, and not restrictive, and the appended claims are intended
to cover all such modifications, enhancements, and other embodiments,
which fall within the true spirit and scope of the description. Thus, to
the maximum extent allowed by law, the scope is to be determined by the
broadest permissible interpretation of the following claims and their
equivalents, and shall not be restricted or limited by the foregoing
detailed description.